{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "sZBpjW61jKTy"
   },
   "source": [
    "### Load Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 83
    },
    "colab_type": "code",
    "id": "QOsL5HBdjTUc",
    "outputId": "e0da7a2c-aba5-40a5-dbb8-40d3b3d63f38"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\HP\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\HP\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# imports\n",
    "import os\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from tqdm import tqdm\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.stats import mode\n",
    "from sklearn import metrics, preprocessing, model_selection\n",
    "from sklearn.model_selection import train_test_split, StratifiedKFold, KFold\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\n",
    "from sklearn import preprocessing, decomposition, model_selection, metrics, pipeline\n",
    "\n",
    "\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "from nltk import word_tokenize\n",
    "nltk.download('punkt')\n",
    "\n",
    "\n",
    "import re\n",
    "import string\n",
    "\n",
    "from nltk.corpus import stopwords \n",
    "stop_words = stopwords.words('english')\n",
    "\n",
    "\n",
    "import lightgbm as lgb\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "pd.options.display.max_columns = 100\n",
    "from plotly import tools\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import init_notebook_mode, iplot\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "T0E0dKbajXfF"
   },
   "source": [
    "### Read the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "69cn7fc71tqo"
   },
   "outputs": [],
   "source": [
    "glove_file = 'F:\\\\MachineHack\\\\identify_author\\\\glove.6B\\\\'\n",
    "\n",
    "glove_file_1 =  'F:\\\\MachineHack\\\\identify_author\\\\glove.840B.300d\\\\'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SIME_wq1jG8E"
   },
   "outputs": [],
   "source": [
    "sub_df = pd.read_excel('Sample_Submission.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 137
    },
    "colab_type": "code",
    "id": "2XKKnGexjQlY",
    "outputId": "b2ae9036-f1b9-4f7d-e7cc-d0209fc47992"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "      <td>119</td>\n",
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       "      <td>108</td>\n",
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      ],
      "text/plain": [
       "   Price\n",
       "0    119\n",
       "1    123\n",
       "2    108"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "s7c_GRIhjnYC",
    "outputId": "771842bb-aa8f-4f24-99cc-93ef1c705393"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1560, 1)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hAq6B9RDjpMC"
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_excel('Data_Train.xlsx', encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 187
    },
    "colab_type": "code",
    "id": "zREPgZn_jtan",
    "outputId": "73f41136-bf7a-440a-985d-68e6ce50a29f"
   },
   "outputs": [
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Title</th>\n",
       "      <th>Author</th>\n",
       "      <th>Edition</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Synopsis</th>\n",
       "      <th>Genre</th>\n",
       "      <th>BookCategory</th>\n",
       "      <th>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Prisoner's Gold (The Hunters 3)</td>\n",
       "      <td>Chris Kuzneski</td>\n",
       "      <td>Paperback,– 10 Mar 2016</td>\n",
       "      <td>4.0 out of 5 stars</td>\n",
       "      <td>8 customer reviews</td>\n",
       "      <td>THE HUNTERS return in their third brilliant no...</td>\n",
       "      <td>Action &amp; Adventure (Books)</td>\n",
       "      <td>Action &amp; Adventure</td>\n",
       "      <td>220.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Guru Dutt: A Tragedy in Three Acts</td>\n",
       "      <td>Arun Khopkar</td>\n",
       "      <td>Paperback,– 7 Nov 2012</td>\n",
       "      <td>3.9 out of 5 stars</td>\n",
       "      <td>14 customer reviews</td>\n",
       "      <td>A layered portrait of a troubled genius for wh...</td>\n",
       "      <td>Cinema &amp; Broadcast (Books)</td>\n",
       "      <td>Biographies, Diaries &amp; True Accounts</td>\n",
       "      <td>202.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Leviathan (Penguin Classics)</td>\n",
       "      <td>Thomas Hobbes</td>\n",
       "      <td>Paperback,– 25 Feb 1982</td>\n",
       "      <td>4.8 out of 5 stars</td>\n",
       "      <td>6 customer reviews</td>\n",
       "      <td>\"During the time men live without a common Pow...</td>\n",
       "      <td>International Relations</td>\n",
       "      <td>Humour</td>\n",
       "      <td>299.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Title          Author  \\\n",
       "0  The Prisoner's Gold (The Hunters 3)  Chris Kuzneski   \n",
       "1   Guru Dutt: A Tragedy in Three Acts    Arun Khopkar   \n",
       "2         Leviathan (Penguin Classics)   Thomas Hobbes   \n",
       "\n",
       "                   Edition             Reviews              Ratings  \\\n",
       "0  Paperback,– 10 Mar 2016  4.0 out of 5 stars   8 customer reviews   \n",
       "1   Paperback,– 7 Nov 2012  3.9 out of 5 stars  14 customer reviews   \n",
       "2  Paperback,– 25 Feb 1982  4.8 out of 5 stars   6 customer reviews   \n",
       "\n",
       "                                            Synopsis  \\\n",
       "0  THE HUNTERS return in their third brilliant no...   \n",
       "1  A layered portrait of a troubled genius for wh...   \n",
       "2  \"During the time men live without a common Pow...   \n",
       "\n",
       "                        Genre                          BookCategory   Price  \n",
       "0  Action & Adventure (Books)                    Action & Adventure  220.00  \n",
       "1  Cinema & Broadcast (Books)  Biographies, Diaries & True Accounts  202.93  \n",
       "2     International Relations                                Humour  299.00  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 117
    },
    "colab_type": "code",
    "id": "rpSzBH1aG-yT",
    "outputId": "a4a35b72-0ebb-4dd7-8006-82bcb297d06e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Paperback,– 5 Oct 2017    48\n",
       "Paperback,– 2016          46\n",
       "Paperback,– 2017          36\n",
       "Paperback,– 2013          31\n",
       "Paperback,– 2019          30\n",
       "Name: Edition, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df['Edition'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JPP7a5FJjwgW"
   },
   "outputs": [],
   "source": [
    "test_df = pd.read_excel('Data_Test.xlsx', encoding='latin-1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 174
    },
    "colab_type": "code",
    "id": "JNEJ5HQQj44d",
    "outputId": "27088b04-0a41-42c7-fcf8-07ae21cf297a"
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Title</th>\n",
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       "      <th>Reviews</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Synopsis</th>\n",
       "      <th>Genre</th>\n",
       "      <th>BookCategory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Complete Sherlock Holmes: 2 Boxes sets</td>\n",
       "      <td>Sir Arthur Conan Doyle</td>\n",
       "      <td>Mass Market Paperback,– 1 Oct 1986</td>\n",
       "      <td>4.4 out of 5 stars</td>\n",
       "      <td>960 customer reviews</td>\n",
       "      <td>A collection of entire body of work of the She...</td>\n",
       "      <td>Short Stories (Books)</td>\n",
       "      <td>Crime, Thriller &amp; Mystery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Learn Docker - Fundamentals of Docker 18.x: Ev...</td>\n",
       "      <td>Gabriel N. Schenker</td>\n",
       "      <td>Paperback,– Import, 26 Apr 2018</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>1 customer review</td>\n",
       "      <td>Enhance your software deployment workflow usin...</td>\n",
       "      <td>Operating Systems Textbooks</td>\n",
       "      <td>Computing, Internet &amp; Digital Media</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Title                  Author  \\\n",
       "0         The Complete Sherlock Holmes: 2 Boxes sets  Sir Arthur Conan Doyle   \n",
       "1  Learn Docker - Fundamentals of Docker 18.x: Ev...     Gabriel N. Schenker   \n",
       "\n",
       "                              Edition             Reviews  \\\n",
       "0  Mass Market Paperback,– 1 Oct 1986  4.4 out of 5 stars   \n",
       "1     Paperback,– Import, 26 Apr 2018  5.0 out of 5 stars   \n",
       "\n",
       "                Ratings                                           Synopsis  \\\n",
       "0  960 customer reviews  A collection of entire body of work of the She...   \n",
       "1     1 customer review  Enhance your software deployment workflow usin...   \n",
       "\n",
       "                         Genre                         BookCategory  \n",
       "0        Short Stories (Books)            Crime, Thriller & Mystery  \n",
       "1  Operating Systems Textbooks  Computing, Internet & Digital Media  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "n5cRpsZUj6f_",
    "outputId": "05289a90-5d12-4d95-e181-46dad23154cb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples in train: 6237\n",
      "Number of columns in train: 9\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of samples in train: {train_df.shape[0]}')\n",
    "print(f'Number of columns in train: {train_df.shape[1]}')\n",
    "for col in train_df.columns:\n",
    "    if train_df[col].isnull().any():\n",
    "        print(col, train_df[col].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "D1u00vPej8Vd",
    "outputId": "5e847512-acca-4963-ed93-fb684564729a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of samples in test: 1560\n",
      "Number of columns in test: 8\n"
     ]
    }
   ],
   "source": [
    "print(f'Number of samples in test: {test_df.shape[0]}')\n",
    "print(f'Number of columns in test: {test_df.shape[1]}')\n",
    "for col in test_df.columns:\n",
    "    if test_df[col].isnull().any():\n",
    "        print(col, test_df[col].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "byFpOkqakAAj",
    "outputId": "9ec3d5c0-d238-4f11-8ea1-a5235345813e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6237, 9)\n",
      "(1560, 8)\n"
     ]
    }
   ],
   "source": [
    "print(train_df.shape)\n",
    "print(test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "D3d0cUwpkxQH",
    "outputId": "31e229b0-bbc8-45ea-8933-b9663c873652"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Title', 'Author', 'Edition', 'Reviews', 'Ratings', 'Synopsis', 'Genre',\n",
       "       'BookCategory', 'Price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "l2SISxwIegFw"
   },
   "source": [
    "### WordClouds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Kd40U9aEfR7N"
   },
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud, STOPWORDS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 482
    },
    "colab_type": "code",
    "id": "1xc0PTBIefHu",
    "outputId": "49703f45-54d4-41ba-f649-5b7ad36db76b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 8))\n",
    "text = ' '.join(train_df['Title'].values)\n",
    "wordcloud = WordCloud(max_font_size=None, background_color='white', width=1200, height=1000).generate(text)\n",
    "plt.imshow(wordcloud)\n",
    "plt.title('Top words in titles')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "i3rR6NTbiaon"
   },
   "outputs": [],
   "source": [
    "def clean_text(text):\n",
    "    text = text.lower()\n",
    "    text = re.sub(r'@[a-zA-Z0-9_]+', '', text)   \n",
    "    text = re.sub(r'https?://[A-Za-z0-9./]+', '', text)   \n",
    "    text = re.sub(r'www.[^ ]+', '', text)  \n",
    "    text = re.sub(r'[a-zA-Z0-9]*www[a-zA-Z0-9]*com[a-zA-Z0-9]*', '', text)  \n",
    "    text = re.sub(r'[^a-zA-Z]', ' ', text)   \n",
    "    text = [token for token in text.split() if len(token) > 2]\n",
    "    text = ' '.join(text)\n",
    "    return text\n",
    "\n",
    "train_df['Synopsis'] = train_df['Synopsis'].apply(clean_text)\n",
    "test_df['Synopsis'] = test_df['Synopsis'].apply(clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MgHykJGfji50"
   },
   "outputs": [],
   "source": [
    "## Number of words in the Synopsis ##\n",
    "train_df[\"Syn_num_words\"] = train_df[\"Synopsis\"].apply(lambda x: len(str(x).split()))\n",
    "test_df[\"Syn_num_words\"] = test_df[\"Synopsis\"].apply(lambda x: len(str(x).split()))\n",
    "\n",
    "## Number of unique words in the Synopsis ##\n",
    "train_df[\"Syn_num_unique_words\"] = train_df[\"Synopsis\"].apply(lambda x: len(set(str(x).split())))\n",
    "test_df[\"Syn_num_unique_words\"] = test_df[\"Synopsis\"].apply(lambda x: len(set(str(x).split())))\n",
    "\n",
    "## Number of characters in the Synopsis ##\n",
    "train_df[\"Syn_num_chars\"] = train_df[\"Synopsis\"].apply(lambda x: len(str(x)))\n",
    "test_df[\"Syn_num_chars\"] = test_df[\"Synopsis\"].apply(lambda x: len(str(x)))\n",
    "\n",
    "## Number of stopwords in the Synopsis ##\n",
    "train_df[\"Syn_num_stopwords\"] = train_df[\"Synopsis\"].apply(lambda x: len([w for w in str(x).lower().split() if w in stop_words]))\n",
    "test_df[\"Syn_num_stopwords\"] = test_df[\"Synopsis\"].apply(lambda x: len([w for w in str(x).lower().split() if w in stop_words]))\n",
    "\n",
    "## Number of punctuations in the Synopsis ##\n",
    "train_df[\"Syn_num_punctuations\"] =train_df['Synopsis'].apply(lambda x: len([c for c in str(x) if c in string.punctuation]) )\n",
    "test_df[\"Syn_num_punctuations\"] =test_df['Synopsis'].apply(lambda x: len([c for c in str(x) if c in string.punctuation]) )\n",
    "\n",
    "## Number of title case words in the Synopsis ##\n",
    "train_df[\"Syn_num_words_upper\"] = train_df[\"Synopsis\"].apply(lambda x: len([w for w in str(x).split() if w.isupper()]))\n",
    "test_df[\"Syn_num_words_upper\"] = test_df[\"Synopsis\"].apply(lambda x: len([w for w in str(x).split() if w.isupper()]))\n",
    "\n",
    "## Number of title case words in the Synopsis ##\n",
    "train_df[\"Syn_num_words_title\"] = train_df[\"Synopsis\"].apply(lambda x: len([w for w in str(x).split() if w.istitle()]))\n",
    "test_df[\"Syn_num_words_title\"] = test_df[\"Synopsis\"].apply(lambda x: len([w for w in str(x).split() if w.istitle()]))\n",
    "\n",
    "## Average length of the words in the Synopsis ##\n",
    "train_df[\"mean_word_len\"] = train_df[\"Synopsis\"].apply(lambda x: np.mean([len(w) for w in str(x).split()]))\n",
    "test_df[\"mean_word_len\"] = test_df[\"Synopsis\"].apply(lambda x: np.mean([len(w) for w in str(x).split()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "f8CxhL0roroP"
   },
   "outputs": [],
   "source": [
    "## Number of words in the Title ##\n",
    "train_df[\"Tit_num_words\"] = train_df[\"Title\"].apply(lambda x: len(str(x).split()))\n",
    "test_df[\"Tit_num_words\"] = test_df[\"Title\"].apply(lambda x: len(str(x).split()))\n",
    "\n",
    "## Number of unique words in the Title ##\n",
    "train_df[\"Tit_num_unique_words\"] = train_df[\"Title\"].apply(lambda x: len(set(str(x).split())))\n",
    "test_df[\"Tit_num_unique_words\"] = test_df[\"Title\"].apply(lambda x: len(set(str(x).split())))\n",
    "\n",
    "## Number of characters in the Title ##\n",
    "train_df[\"Tit_num_chars\"] = train_df[\"Title\"].apply(lambda x: len(str(x)))\n",
    "test_df[\"Tit_num_chars\"] = test_df[\"Title\"].apply(lambda x: len(str(x)))\n",
    "\n",
    "## Number of stopwords in the Title ##\n",
    "train_df[\"Tit_num_stopwords\"] = train_df[\"Title\"].apply(lambda x: len([w for w in str(x).lower().split() if w in stop_words]))\n",
    "test_df[\"Tit_num_stopwords\"] = test_df[\"Title\"].apply(lambda x: len([w for w in str(x).lower().split() if w in stop_words]))\n",
    "\n",
    "## Number of punctuations in the Title ##\n",
    "train_df[\"Tit_num_punctuations\"] =train_df['Title'].apply(lambda x: len([c for c in str(x) if c in string.punctuation]) )\n",
    "test_df[\"Tit_num_punctuations\"] =test_df['Title'].apply(lambda x: len([c for c in str(x) if c in string.punctuation]) )\n",
    "\n",
    "## Number of title case words in the Title ##\n",
    "train_df[\"Tit_num_words_upper\"] = train_df[\"Title\"].apply(lambda x: len([w for w in str(x).split() if w.isupper()]))\n",
    "test_df[\"Tit_num_words_upper\"] = test_df[\"Title\"].apply(lambda x: len([w for w in str(x).split() if w.isupper()]))\n",
    "\n",
    "## Number of title case words in the Title ##\n",
    "train_df[\"Tit_num_words_title\"] = train_df[\"Title\"].apply(lambda x: len([w for w in str(x).split() if w.istitle()]))\n",
    "test_df[\"Tit_num_words_title\"] = test_df[\"Title\"].apply(lambda x: len([w for w in str(x).split() if w.istitle()]))\n",
    "\n",
    "## Average length of the words in the Title ##\n",
    "train_df[\"mean_word_len\"] = train_df[\"Title\"].apply(lambda x: np.mean([len(w) for w in str(x).split()]))\n",
    "test_df[\"mean_word_len\"] = test_df[\"Title\"].apply(lambda x: np.mean([len(w) for w in str(x).split()]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5FDk5wwSk7hp"
   },
   "outputs": [],
   "source": [
    "# * join the datasets\n",
    "train_df['is_train']  = 1\n",
    "test_df['Price'] = 0\n",
    "test_df['is_train'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Vv6xKwuIlQw5"
   },
   "outputs": [],
   "source": [
    "full_df = train_df.append(test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 634
    },
    "colab_type": "code",
    "id": "WKQ23t8ilURU",
    "outputId": "5c1fc7b6-2ac9-4915-f66d-d84681b81e8e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Author</th>\n",
       "      <th>BookCategory</th>\n",
       "      <th>Edition</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Price</th>\n",
       "      <th>Ratings</th>\n",
       "      <th>Reviews</th>\n",
       "      <th>Syn_num_chars</th>\n",
       "      <th>Syn_num_punctuations</th>\n",
       "      <th>Syn_num_stopwords</th>\n",
       "      <th>Syn_num_unique_words</th>\n",
       "      <th>Syn_num_words</th>\n",
       "      <th>Syn_num_words_title</th>\n",
       "      <th>Syn_num_words_upper</th>\n",
       "      <th>Synopsis</th>\n",
       "      <th>Tit_num_chars</th>\n",
       "      <th>Tit_num_punctuations</th>\n",
       "      <th>Tit_num_stopwords</th>\n",
       "      <th>Tit_num_unique_words</th>\n",
       "      <th>Tit_num_words</th>\n",
       "      <th>Tit_num_words_title</th>\n",
       "      <th>Tit_num_words_upper</th>\n",
       "      <th>Title</th>\n",
       "      <th>is_train</th>\n",
       "      <th>mean_word_len</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chris Kuzneski</td>\n",
       "      <td>Action &amp; Adventure</td>\n",
       "      <td>Paperback,– 10 Mar 2016</td>\n",
       "      <td>Action &amp; Adventure (Books)</td>\n",
       "      <td>220.00</td>\n",
       "      <td>8 customer reviews</td>\n",
       "      <td>4.0 out of 5 stars</td>\n",
       "      <td>705</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>87</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>the hunters return their third brilliant novel...</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>The Prisoner's Gold (The Hunters 3)</td>\n",
       "      <td>1</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Arun Khopkar</td>\n",
       "      <td>Biographies, Diaries &amp; True Accounts</td>\n",
       "      <td>Paperback,– 7 Nov 2012</td>\n",
       "      <td>Cinema &amp; Broadcast (Books)</td>\n",
       "      <td>202.93</td>\n",
       "      <td>14 customer reviews</td>\n",
       "      <td>3.9 out of 5 stars</td>\n",
       "      <td>1032</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>110</td>\n",
       "      <td>159</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>layered portrait troubled genius for whom art ...</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>Guru Dutt: A Tragedy in Three Acts</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thomas Hobbes</td>\n",
       "      <td>Humour</td>\n",
       "      <td>Paperback,– 25 Feb 1982</td>\n",
       "      <td>International Relations</td>\n",
       "      <td>299.00</td>\n",
       "      <td>6 customer reviews</td>\n",
       "      <td>4.8 out of 5 stars</td>\n",
       "      <td>1482</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>157</td>\n",
       "      <td>211</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>during the time men live without common power ...</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>Leviathan (Penguin Classics)</td>\n",
       "      <td>1</td>\n",
       "      <td>8.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Agatha Christie</td>\n",
       "      <td>Crime, Thriller &amp; Mystery</td>\n",
       "      <td>Paperback,– 5 Oct 2017</td>\n",
       "      <td>Contemporary Fiction (Books)</td>\n",
       "      <td>180.00</td>\n",
       "      <td>13 customer reviews</td>\n",
       "      <td>4.1 out of 5 stars</td>\n",
       "      <td>353</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>47</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>handful grain found the pocket murdered busine...</td>\n",
       "      <td>34</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>A Pocket Full of Rye (Miss Marple)</td>\n",
       "      <td>1</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Editors of Life</td>\n",
       "      <td>Arts, Film &amp; Photography</td>\n",
       "      <td>Hardcover,– 10 Oct 2006</td>\n",
       "      <td>Photography Textbooks</td>\n",
       "      <td>965.62</td>\n",
       "      <td>1 customer review</td>\n",
       "      <td>5.0 out of 5 stars</td>\n",
       "      <td>586</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>66</td>\n",
       "      <td>88</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>for seven decades life has been thrilling the ...</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>LIFE 70 Years of Extraordinary Photography</td>\n",
       "      <td>1</td>\n",
       "      <td>6.166667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Author                          BookCategory  \\\n",
       "0   Chris Kuzneski                    Action & Adventure   \n",
       "1     Arun Khopkar  Biographies, Diaries & True Accounts   \n",
       "2    Thomas Hobbes                                Humour   \n",
       "3  Agatha Christie             Crime, Thriller & Mystery   \n",
       "4  Editors of Life              Arts, Film & Photography   \n",
       "\n",
       "                   Edition                         Genre   Price  \\\n",
       "0  Paperback,– 10 Mar 2016    Action & Adventure (Books)  220.00   \n",
       "1   Paperback,– 7 Nov 2012    Cinema & Broadcast (Books)  202.93   \n",
       "2  Paperback,– 25 Feb 1982       International Relations  299.00   \n",
       "3   Paperback,– 5 Oct 2017  Contemporary Fiction (Books)  180.00   \n",
       "4  Hardcover,– 10 Oct 2006         Photography Textbooks  965.62   \n",
       "\n",
       "               Ratings             Reviews  Syn_num_chars  \\\n",
       "0   8 customer reviews  4.0 out of 5 stars            705   \n",
       "1  14 customer reviews  3.9 out of 5 stars           1032   \n",
       "2   6 customer reviews  4.8 out of 5 stars           1482   \n",
       "3  13 customer reviews  4.1 out of 5 stars            353   \n",
       "4    1 customer review  5.0 out of 5 stars            586   \n",
       "\n",
       "   Syn_num_punctuations  Syn_num_stopwords  Syn_num_unique_words  \\\n",
       "0                     0                 37                    87   \n",
       "1                     0                 40                   110   \n",
       "2                     0                 66                   157   \n",
       "3                     0                 16                    47   \n",
       "4                     0                 33                    66   \n",
       "\n",
       "   Syn_num_words  Syn_num_words_title  Syn_num_words_upper  \\\n",
       "0            112                    0                    0   \n",
       "1            159                    0                    0   \n",
       "2            211                    0                    0   \n",
       "3             54                    0                    0   \n",
       "4             88                    0                    0   \n",
       "\n",
       "                                            Synopsis  Tit_num_chars  \\\n",
       "0  the hunters return their third brilliant novel...             35   \n",
       "1  layered portrait troubled genius for whom art ...             34   \n",
       "2  during the time men live without common power ...             28   \n",
       "3  handful grain found the pocket murdered busine...             34   \n",
       "4  for seven decades life has been thrilling the ...             42   \n",
       "\n",
       "   Tit_num_punctuations  Tit_num_stopwords  Tit_num_unique_words  \\\n",
       "0                     3                  1                     6   \n",
       "1                     1                  2                     7   \n",
       "2                     2                  0                     3   \n",
       "3                     2                  2                     7   \n",
       "4                     0                  1                     6   \n",
       "\n",
       "   Tit_num_words  Tit_num_words_title  Tit_num_words_upper  \\\n",
       "0              6                    4                    0   \n",
       "1              7                    6                    1   \n",
       "2              3                    3                    0   \n",
       "3              7                    6                    1   \n",
       "4              6                    3                    1   \n",
       "\n",
       "                                        Title  is_train  mean_word_len  \n",
       "0         The Prisoner's Gold (The Hunters 3)         1       5.000000  \n",
       "1          Guru Dutt: A Tragedy in Three Acts         1       4.000000  \n",
       "2                Leviathan (Penguin Classics)         1       8.666667  \n",
       "3          A Pocket Full of Rye (Miss Marple)         1       4.000000  \n",
       "4  LIFE 70 Years of Extraordinary Photography         1       6.166667  "
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fb0jHYM5J6qs"
   },
   "outputs": [],
   "source": [
    "full_df[['Edition_Type','Date']] = full_df.Edition.astype(str).str.split(\",–\", n = 1, expand = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D8-qeX3wMFju"
   },
   "outputs": [],
   "source": [
    "full_df['Year'] = full_df['Date'].apply(lambda x: x[-5:] if x[-1].isdigit() else np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "V3tCaFS0RO8u"
   },
   "outputs": [],
   "source": [
    "full_df['Reviews'] = full_df['Reviews'].apply(lambda x: x.replace('out of 5 stars', ''))\n",
    "full_df['Ratings'] = full_df['Ratings'].apply(lambda x: x.replace('customer reviews', ''))\n",
    "full_df['Ratings'] = full_df['Ratings'].apply(lambda x: x.replace('customer review', ''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     10 Mar 2016\n",
       "1      7 Nov 2012\n",
       "2     25 Feb 1982\n",
       "3      5 Oct 2017\n",
       "4     10 Oct 2006\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_df['Date'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Oct    795\n",
       "Sep    696\n",
       "May    668\n",
       "Jan    637\n",
       "Nov    625\n",
       "Apr    605\n",
       "Jun    605\n",
       "Jul    580\n",
       "Mar    574\n",
       "Aug    544\n",
       "Feb    508\n",
       "Dec    503\n",
       "       418\n",
       "ort     15\n",
       "set      7\n",
       "ted      6\n",
       "int      5\n",
       "ion      2\n",
       "ile      1\n",
       "ook      1\n",
       "TSC      1\n",
       "ged      1\n",
       "Name: Month, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# code to find characters in string \n",
    "def find_chars(x):\n",
    "  if(len(str(x))>0):\n",
    "    return \" \".join(re.split(\"[^a-zA-Z]*\", x))\n",
    "  else:\n",
    "    return \"\"  \n",
    "\n",
    "full_df['Month'] = full_df['Date'].apply(lambda x: find_chars(x))\n",
    "\n",
    "def last_3_chars(x):\n",
    "  if (len(x)>0):\n",
    "    x = ''.join(x.split())\n",
    "    return x[-3:]\n",
    "  else:\n",
    "    return ''  \n",
    "\n",
    "\n",
    "full_df['Month'] = full_df['Month'].apply(lambda x: last_3_chars(x))\n",
    "full_df['Month'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EFG_gAgVU4Al"
   },
   "outputs": [],
   "source": [
    "def is_Paperback(x):\n",
    "  if (str(x).find('Paperback') >-1):\n",
    "    return 'Paperback'\n",
    "  elif (str(x).find('Hardcover') >-1):\n",
    "    return 'Hardcover'\n",
    "  else:\n",
    "    return 'Other'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6a6IPRWecVQx"
   },
   "outputs": [],
   "source": [
    "full_df['Edition_Type'] = full_df['Edition_Type'].apply(lambda x: is_Paperback(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 83
    },
    "colab_type": "code",
    "id": "n6QyOUh4VxzX",
    "outputId": "c8b42303-e11f-4345-b37a-b7b128afb69d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Paperback    6664\n",
       "Hardcover    1056\n",
       "Other          77\n",
       "Name: Edition_Type, dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_df['Edition_Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "hXx74DJclZMu",
    "outputId": "e067feed-1ff2-46da-d88b-2ff60032f1d7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6237\n"
     ]
    }
   ],
   "source": [
    "testcount = len(test_df)\n",
    "count = len(full_df)-testcount\n",
    "print(count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "PehyxGBclcPg",
    "outputId": "cc244c39-5a6a-49bc-dc40-a0c0562b70c9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7797, 29)\n"
     ]
    }
   ],
   "source": [
    "print(full_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nZpJJT2V0Rqq"
   },
   "outputs": [],
   "source": [
    "dfDummies = pd.get_dummies(full_df['BookCategory'], prefix = 'BookCategory')\n",
    "full_df = pd.concat([full_df, dfDummies], axis=1)\n",
    "\n",
    "dfDummies = pd.get_dummies(full_df['Edition_Type'], prefix = 'Edition_Type')\n",
    "full_df = pd.concat([full_df, dfDummies], axis=1)\n",
    "\n",
    "\n",
    "dfDummies = pd.get_dummies(full_df['Month'], prefix = 'Month')\n",
    "full_df = pd.concat([full_df, dfDummies], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "bjpzaQg6lw8G",
    "outputId": "25c6c79f-5ee4-47a2-baa4-cd804440e12e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6237, 65) (1560, 65)\n"
     ]
    }
   ],
   "source": [
    "# FinalData= FinalData.drop(cols,axis=1)\n",
    "train = full_df[:count]\n",
    "test = full_df[count:]\n",
    "print(train.shape, test.shape)\n",
    "\n",
    "\n",
    "train_df = train.copy()\n",
    "test_df = test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7ZFl45qW30h8"
   },
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "trim_function = lambda x : re.findall(\"^\\s*(.*?)\\s*$\",str(x))[0]\n",
    "\n",
    "remove_commas = lambda x: re.sub(\"[^\\d]\", \"\", str(x))\n",
    "\n",
    "\n",
    "train_df['Ratings']= train_df['Ratings'].apply(trim_function).apply(remove_commas).astype(int)\n",
    "test_df['Ratings']= test_df['Ratings'].apply(trim_function).apply(remove_commas).astype(int)\n",
    "\n",
    "train_df['Reviews']= train_df['Reviews'].apply(trim_function).apply(remove_commas).astype(int)\n",
    "test_df['Reviews']= test_df['Reviews'].apply(trim_function).apply(remove_commas).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ciuw0t-oB7th"
   },
   "outputs": [],
   "source": [
    "### Fit transform the tfidf vectorizer ###\n",
    "tfidf_vec = TfidfVectorizer(stop_words='english', ngram_range=(1,4),max_features=200)\n",
    "full_tfidf = tfidf_vec.fit_transform(train_df['Synopsis'].values.tolist() + test_df['Synopsis'].values.tolist())\n",
    "train_tfidf = tfidf_vec.transform(train_df['Synopsis'].values.tolist())\n",
    "test_tfidf = tfidf_vec.transform(test_df['Synopsis'].values.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7XpaCVjgSUxH"
   },
   "outputs": [],
   "source": [
    "train_tfidf = pd.DataFrame(train_tfidf.toarray(), columns=tfidf_vec.get_feature_names())\n",
    "test_tfidf = pd.DataFrame(test_tfidf.toarray(), columns=tfidf_vec.get_feature_names())\n",
    "\n",
    "train_df = pd.concat([train_df, train_tfidf], axis=1)\n",
    "test_df = pd.concat([test_df, test_tfidf], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "C5h8bE9aOG7e",
    "outputId": "4b59f2ce-40a0-4d1c-9e3a-8e1aba8890a6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1560, 265)"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_eFSDW5oZD4A"
   },
   "outputs": [],
   "source": [
    "### Fit transform the tfidf vectorizer ###\n",
    "tfidf_vec = TfidfVectorizer(stop_words='english', ngram_range=(1,4),max_features=150)\n",
    "full_tfidf = tfidf_vec.fit_transform(train_df['Title'].values.tolist() + test_df['Title'].values.tolist())\n",
    "train_tfidf = tfidf_vec.transform(train_df['Title'].values.tolist())\n",
    "test_tfidf = tfidf_vec.transform(test_df['Title'].values.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WfaEu-x0ZahD"
   },
   "outputs": [],
   "source": [
    "train_tfidf = pd.DataFrame(train_tfidf.toarray(), columns=tfidf_vec.get_feature_names())\n",
    "test_tfidf = pd.DataFrame(test_tfidf.toarray(), columns=tfidf_vec.get_feature_names())\n",
    "\n",
    "train_df = pd.concat([train_df, train_tfidf], axis=1)\n",
    "test_df = pd.concat([test_df, test_tfidf], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vRf3milepkCb"
   },
   "outputs": [],
   "source": [
    "### Fit transform the CountVectorizer ###\n",
    "tfidf_vec = CountVectorizer(ngram_range=(1,7), analyzer='char', max_features=100)\n",
    "tfidf_vec.fit(train_df['Title'].values.tolist() + test_df['Title'].values.tolist())\n",
    "train_tfidf = tfidf_vec.transform(train_df['Title'].values.tolist())\n",
    "test_tfidf = tfidf_vec.transform(test_df['Title'].values.tolist())\n",
    "\n",
    "\n",
    "train_tfidf = pd.DataFrame(train_tfidf.toarray(), columns=tfidf_vec.get_feature_names())\n",
    "test_tfidf = pd.DataFrame(test_tfidf.toarray(), columns=tfidf_vec.get_feature_names())\n",
    "\n",
    "train_df = pd.concat([train_df, train_tfidf], axis=1)\n",
    "test_df = pd.concat([test_df, test_tfidf], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Oq9_KAqZfTVY"
   },
   "outputs": [],
   "source": [
    "### Fit transform the CountVectorizer ###\n",
    "tfidf_vec = CountVectorizer(ngram_range=(1,7), analyzer='char', max_features=100)\n",
    "tfidf_vec.fit(train_df['Synopsis'].values.tolist() + test_df['Synopsis'].values.tolist())\n",
    "train_tfidf = tfidf_vec.transform(train_df['Synopsis'].values.tolist())\n",
    "test_tfidf = tfidf_vec.transform(test_df['Synopsis'].values.tolist())\n",
    "\n",
    "\n",
    "train_tfidf = pd.DataFrame(train_tfidf.toarray(), columns=tfidf_vec.get_feature_names())\n",
    "test_tfidf = pd.DataFrame(test_tfidf.toarray(), columns=tfidf_vec.get_feature_names())\n",
    "\n",
    "train_df = pd.concat([train_df, train_tfidf], axis=1)\n",
    "test_df = pd.concat([test_df, test_tfidf], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7d31fY-g1i8e"
   },
   "source": [
    "### Glove Vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 50
    },
    "colab_type": "code",
    "id": "K8GIwEHS1le2",
    "outputId": "ecb2157f-d116-42eb-fc7d-ad4bb008bc4e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "2196017it [08:39, 4229.89it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 2196016 word vectors.\n"
     ]
    }
   ],
   "source": [
    "# load the GloVe vectors in a dictionary:\n",
    "\n",
    "embeddings_index = {}\n",
    "f = open(glove_file_1+'glove.840B.300d.txt', encoding='utf8')\n",
    "for line in tqdm(f):\n",
    "    values = line.split(' ')\n",
    "    word = values[0]\n",
    "    coefs = np.asarray(values[1:], dtype='float32')\n",
    "    embeddings_index[word] = coefs\n",
    "f.close()\n",
    "\n",
    "print('Found %s word vectors.' % len(embeddings_index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BuoMibXo2fkI"
   },
   "outputs": [],
   "source": [
    "# this function creates a normalized vector for the whole sentence\n",
    "def sent2vec(s):\n",
    "    words = str(s).lower()\n",
    "    words = word_tokenize(words)\n",
    "    words = [w for w in words if not w in stop_words]\n",
    "    words = [w for w in words if w.isalpha()]\n",
    "    M = []\n",
    "    for w in words:\n",
    "        try:\n",
    "            M.append(embeddings_index[w])\n",
    "        except:\n",
    "            continue\n",
    "    M = np.array(M)\n",
    "    v = M.sum(axis=0)\n",
    "    if type(v) != np.ndarray:\n",
    "        return np.zeros(300)\n",
    "    return v / np.sqrt((v ** 2).sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 150
    },
    "colab_type": "code",
    "id": "_UKMt32S2tV0",
    "outputId": "49cd700d-e558-4c39-9af2-ed33b3671e58"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "400000it [00:36, 11077.20it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 400000 word vectors.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6237, 100)\n"
     ]
    }
   ],
   "source": [
    "# create sentence vectors using the above function for training and validation set\n",
    "xtrain_glove = [sent2vec(x) for x in tqdm(train_df['Synopsis'].values)]\n",
    "xtest_glove = [sent2vec(x) for x in tqdm(test_df['Synopsis'].values)]\n",
    "\n",
    "xtrain_glove = np.array(xtrain_glove)\n",
    "xtest_glove = np.array(xtest_glove)\n",
    "\n",
    "xtrain_glove = pd.DataFrame(xtrain_glove)\n",
    "xtest_glove = pd.DataFrame(xtest_glove)\n",
    "\n",
    "print(xtrain_glove.shape)\n",
    "\n",
    "xtrain_glove.columns = ['glove_syn_'+str(i) for i in range(300)]\n",
    "xtest_glove.columns = ['glove_syn_'+str(i) for i in range(300)]\n",
    "\n",
    "train_df = pd.concat([train_df, xtrain_glove], axis=1)\n",
    "test_df = pd.concat([test_df, xtest_glove], axis=1)\n",
    "\n",
    "\n",
    "# load the GloVe vectors in a dictionary:\n",
    "\n",
    "embeddings_index = {}\n",
    "f = open(glove_file+'glove.6B.100d.txt', encoding='utf8')\n",
    "for line in tqdm(f):\n",
    "    values = line.split()\n",
    "    word = values[0]\n",
    "    coefs = np.asarray(values[1:], dtype='float32')\n",
    "    embeddings_index[word] = coefs\n",
    "f.close()\n",
    "\n",
    "print('Found %s word vectors.' % len(embeddings_index))\n",
    "\n",
    "\n",
    "# this function creates a normalized vector for the whole sentence\n",
    "def sent2vec_genre(s):\n",
    "    words = str(s).lower()\n",
    "    words = word_tokenize(words)\n",
    "    words = [w for w in words if not w in stop_words]\n",
    "    words = [w for w in words if w.isalpha()]\n",
    "    M = []\n",
    "    for w in words:\n",
    "        try:\n",
    "            M.append(embeddings_index[w])\n",
    "        except:\n",
    "            continue\n",
    "    M = np.array(M)\n",
    "    v = M.sum(axis=0)\n",
    "    if type(v) != np.ndarray:\n",
    "        return np.zeros(100)\n",
    "    return v / np.sqrt((v ** 2).sum())\n",
    "\n",
    "\n",
    "# create sentence vectors using the above function for training and validation set\n",
    "xtrain_glove = [sent2vec_genre(x) for x in tqdm(train_df['Genre'].values)]\n",
    "xtest_glove = [sent2vec_genre(x) for x in tqdm(test_df['Genre'].values)]\n",
    "\n",
    "xtrain_glove = np.array(xtrain_glove)\n",
    "xtest_glove = np.array(xtest_glove)\n",
    "\n",
    "xtrain_glove = pd.DataFrame(xtrain_glove)\n",
    "xtest_glove = pd.DataFrame(xtest_glove)\n",
    "\n",
    "print(xtrain_glove.shape)\n",
    "\n",
    "xtrain_glove.columns = ['glove_genre_'+str(i) for i in range(100)]\n",
    "xtest_glove.columns = ['glove_genre_'+str(i) for i in range(100)]\n",
    "\n",
    "train_df = pd.concat([train_df, xtrain_glove], axis=1)\n",
    "test_df = pd.concat([test_df, xtest_glove], axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "9F0tK17QwkZ9",
    "outputId": "df4e0700-56c9-4eee-e94a-1aae5ff97e99"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6237, 100)\n"
     ]
    }
   ],
   "source": [
    "# this function creates a normalized vector for the whole sentence\n",
    "def sent2vec_bk(s):\n",
    "    words = str(s).lower()\n",
    "    words = word_tokenize(words)\n",
    "    words = [w for w in words if not w in stop_words]\n",
    "    words = [w for w in words if w.isalpha()]\n",
    "    M = []\n",
    "    for w in words:\n",
    "        try:\n",
    "            M.append(embeddings_index[w])\n",
    "        except:\n",
    "            continue\n",
    "    M = np.array(M)\n",
    "    v = M.sum(axis=0)\n",
    "    if type(v) != np.ndarray:\n",
    "        return np.zeros(100)\n",
    "    return v / np.sqrt((v ** 2).sum())\n",
    "\n",
    "# create sentence vectors using the above function for training and validation set\n",
    "xtrain_glove = [sent2vec_bk(x) for x in tqdm(train_df['BookCategory'].values)]\n",
    "xtest_glove = [sent2vec_bk(x) for x in tqdm(test_df['BookCategory'].values)]\n",
    "\n",
    "xtrain_glove = np.array(xtrain_glove)\n",
    "xtest_glove = np.array(xtest_glove)\n",
    "\n",
    "xtrain_glove = pd.DataFrame(xtrain_glove)\n",
    "xtest_glove = pd.DataFrame(xtest_glove)\n",
    "\n",
    "print(xtrain_glove.shape)\n",
    "\n",
    "xtrain_glove.columns = ['glove_bc_'+str(i) for i in range(100)]\n",
    "xtest_glove.columns = ['glove_bc_'+str(i) for i in range(100)]\n",
    "\n",
    "train_df = pd.concat([train_df, xtrain_glove], axis=1)\n",
    "test_df = pd.concat([test_df, xtest_glove], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 100
    },
    "colab_type": "code",
    "id": "KyaWDs7nsb-7",
    "outputId": "cd123a95-0009-411c-a8f0-4a0b32c246b7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Title\n",
      "Author\n",
      "Synopsis\n",
      "Genre\n",
      "Year\n",
      "Month\n"
     ]
    }
   ],
   "source": [
    "cols = ['Title', 'Author',   'Synopsis', 'Genre',\n",
    "#        'BookCategory', 'Edition_Type',\n",
    "        'Year', 'Month'\n",
    "       ]\n",
    "for col in cols:\n",
    "    if train_df[col].dtype==object:\n",
    "        print(col)\n",
    "        lbl = preprocessing.LabelEncoder()\n",
    "        lbl.fit(list(train_df[col].values.astype('str')) + list(test_df[col].values.astype('str')))\n",
    "        train_df[col] = lbl.transform(list(train_df[col].values.astype('str')))\n",
    "        test_df[col] = lbl.transform(list(test_df[col].values.astype('str')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 390
    },
    "colab_type": "code",
    "id": "MAqoJxrSS4Lh",
    "outputId": "0c784e4d-bad6-4b62-e2e8-9f5eb0ed405e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2tRnYPHetkyRp7nmpsCRJkiSp1wyukiRJkqReM7hKkiRJknrN4CpJkiRJ6jWDqyRJkiSp1wyukiRJkqReM7hKkiRJknptRsE1ya1JPp/kuiTbW9khSbYl2dHuV7TyJDkvyc4kNyQ5dmA/G1r9HUk2zE+XJEmSJEnjZDbfuP50VR1TVeva+ibgyqpaC1zZ1gFOAda220bgfOiCLnAO8HzgOOCcibArSZIkSdJkHsmlwuuBLW15C3DaQPlF1bkKWJ7kCOAkYFtV7auqu4FtwMmP4PklSZIkSUvATINrAR9Pcm2Sja3s8Kq6A6DdH9bKVwG3Dzx2VyubrFySJEmSpEktm2G9F1TV7iSHAduSfHGKuhlSVlOUP/TBXTDeCPC0pz1ths2TJEmSJI2rGX3jWlW72/0e4EN0v1G9s10CTLvf06rvAlYPPPxIYPcU5fs/1wVVta6q1q1cuXJ2vZEkSZIkjZ1pg2uSxyd54sQycCJwI7AVmJgZeANweVveCpzRZhc+Hri3XUr8MeDEJCvapEwntjJJkiRJkiY1k0uFDwc+lGSi/vur6qNJrgEuTXIWcBvw8lb/CuBUYCdwH3AmQFXtS/JW4JpW7y1VtW/OeiJJkiRJGkvTBtequgV47pDyrwMnDCkv4OxJ9rUZ2Dz7ZkqSJEmSlqpH8udwJEmSJEmadzOdVViSJElizaaPjLoJkpYgv3GVJEmSJPWawVWSJEmS1GsGV0mSJElSrxlcJUmSJEm9ZnCVJEmSJPWawVWSJEmS1GsGV0mSJElSrxlcJUmSJEm9ZnCVJEmSJPWawVWSJEmS1GsGV0mSJElSrxlcJUmSJEm9ZnCVJEmSJPWawVWSJEmS1GsGV0mSJElSrxlcJUmSJEm9ZnCVJEmSJPWawVWSJEmS1GsGV0mSJElSrxlcJUmSJEm9ZnCVJEmSJPWawVWSJEmS1GsGV0mSJElSrxlcJUmSJEm9ZnCVJEmSJPWawVWSJEmS1GsGV0mSJElSrxlcJUmSJEm9ZnCVJEmSJPWawVWSJEmS1GsGV0mSJElSrxlcJUmSJEm9ZnCVJEmSJPWawVWSJEmS1GsGV0mSJElSrxlcJUmSJEm9ZnCVJEmSJPWawVWSJEmS1GsGV0mSJElSrxlcJUmSJEm9ZnCVJEmSJPWawVWSJEmS1GszDq5JDkryuSQfbutHJbk6yY4kH0hycCt/dFvf2bavGdjHG1v5l5KcNNedkSRJkiSNn9l84/oa4OaB9bcD76yqtcDdwFmt/Czg7qp6JvDOVo8kRwOnA88BTgbeleSgR9Z8SZKWniSbk+xJcuNA2ZuTfDXJde126sC2oSeOk5zcynYm2bTQ/ZAkaaZmFFyTHAm8BHh3Ww/wYuCyVmULcFpbXt/WadtPaPXXA5dU1Xeq6svATuC4ueiEJElLzHvoTgLv751VdUy7XQGTnzhuJ4//GDgFOBp4RasrSVLvzPQb1/8KvAH4QVt/CnBPVd3f1ncBq9ryKuB2gLb93lb/gfIhj5EkSTNUVZ8G9s2w+mQnjo8DdlbVLVX1XeCSVleSpN6ZNrgmeSmwp6quHSweUrWm2TbVYwafb2OS7Um27927d7rmSZKkB706yQ3tUuIVrWyyE8czPqHs2CxJGrWZfOP6AuBlSW6lOxv7YrpvYJcnWdbqHAnsbsu7gNUAbfuT6c4KP1A+5DEPqKoLqmpdVa1buXLlrDskSdISdT7wI8AxwB3AH7byR3RCGRybJUmjN21wrao3VtWRVbWG7jcyn6iqVwKfBH6+VdsAXN6Wt7Z12vZPVFW18tPbrMNHAWuBz8xZTyRJWsKq6s6q+n5V/QD4Ux6cR2KyE8czOqEsSVIfPJK/4/pbwOuS7KT7DeuFrfxC4Cmt/HXAJoCqugm4FPgC8FHg7Kr6/iN4fkmS1CQ5YmD154CJGYcnO3F8DbC2/Xm7g+lOTm9dyDZLkjRTy6av8qCq+hTwqbZ8C0NmBa6qfwZePsnj3wa8bbaNlCRJD0pyMfAi4NAku4BzgBclOYbuct9bgX8H3YnjJBMnju9n4MRxklcDHwMOAja3k8ySJPXOrIKrJEkavap6xZDiC4eUTdQfeuK4/cmcK+awaZIkzYtHcqmwJEmSJEnzzuAqSZIkSeo1g6skSZIkqdcMrpIkSZKkXjO4SpIkSZJ6zeAqSZIkSeo1g6skSZIkqdcMrpIkSZKkXjO4SpIkSZJ6zeAqSZIkSeo1g6skSZIkqdcMrpIkSZKkXjO4SpIkSZJ6zeAqSZIkSeo1g6skSZIkqdcMrpIkSZKkXjO4SpIkSZJ6zeAqSZIkSeo1g6skSZIkqdcMrpIkSZKkXjO4SpIkSZJ6zeAqSZIkSeo1g6skSZIkqdcMrpIkSZKkXjO4SpIkSZJ6zeAqSZIkSeo1g6skSZIkqdcMrpIkSZKkXjO4SpIkSZJ6zeAqSZIkSeo1g6skSZIkqdcMrpIkSZKkXjO4SpIkSZJ6zeAqSZIkSeo1g6skSZIkqdcMrpIkSZKkXjO4SpIkSZJ6zeAqSZIkSeo1g6skSZIkqdcMrpIkSZKkXjO4SpIkSZJ6zeAqSZIkSeo1g6skSZIkqdemDa5JHpPkM0muT3JTkt9r5UcluTrJjiQfSHJwK390W9/Ztq8Z2NcbW/mXkpw0X52SJEmSJI2PmXzj+h3gxVX1XOAY4OQkxwNvB95ZVWuBu4GzWv2zgLur6pnAO1s9khwNnA48BzgZeFeSg+ayM5IkSZKk8TNtcK3Ot9rqo9qtgBcDl7XyLcBpbXl9W6dtPyFJWvklVfWdqvoysBM4bk56IUmSJEkaWzP6jWuSg5JcB+wBtgH/CNxTVfe3KruAVW15FXA7QNt+L/CUwfIhj5EkSZIkaagZBdeq+n5VHQMcSfct6bOHVWv3mWTbZOUPkWRjku1Jtu/du3cmzZMkSZIkjbFZzSpcVfcAnwKOB5YnWdY2HQnsbsu7gNUAbfuTgX2D5UMeM/gcF1TVuqpat3Llytk0T5IkSZI0hmYyq/DKJMvb8mOBnwFuBj4J/HyrtgG4vC1vbeu07Z+oqmrlp7dZh48C1gKfmauOSJIkSZLG07Lpq3AEsKXNAPxDwKVV9eEkXwAuSfL7wOeAC1v9C4H3JtlJ903r6QBVdVOSS4EvAPcDZ1fV9+e2O5IkSZKkcTNtcK2qG4DnDSm/hSGzAlfVPwMvn2RfbwPeNvtmSpIkSZKWqln9xlWSJEmSpIVmcJUkSZIk9ZrBVZIkSZLUawZXSZIkSVKvGVwlSZIkSb1mcJUkSZIk9ZrBVZIkSZLUawZXSZIkSVKvGVwlSZIkSb1mcJUkaZFJsjnJniQ3DpQdkmRbkh3tfkUrT5LzkuxMckOSYwces6HV35Fkwyj6IknSTBhcJUlafN4DnLxf2SbgyqpaC1zZ1gFOAda220bgfOiCLnAO8HzgOOCcibArSVLfGFwlSVpkqurTwL79itcDW9ryFuC0gfKLqnMVsDzJEcBJwLaq2ldVdwPbODAMS5LUCwZXSZLGw+FVdQdAuz+sla8Cbh+ot6uVTVYuSVLvGFwlSRpvGVJWU5QfuINkY5LtSbbv3bt3ThsnSdJMGFwlSRoPd7ZLgGn3e1r5LmD1QL0jgd1TlB+gqi6oqnVVtW7lypVz3nBJkqZjcJUkaTxsBSZmBt4AXD5QfkabXfh44N52KfHHgBOTrGiTMp3YyiRJ6p1lo26AJEmanSQXAy8CDk2yi2524HOBS5OcBdwGvLxVvwI4FdgJ3AecCVBV+5K8Fbim1XtLVe0/4ZMkSb1gcJUkaZGpqldMsumEIXULOHuS/WwGNs9h0yRJmhdeKixJkiRJ6jWDqyRJkiSp17xUWJIkSZpjazZ9ZNRNOMCt575k1E2QHja/cZUkSZIk9ZrBVZIkSZLUawZXSZIkSVKvGVwlSZIkSb1mcJUkSZIk9ZrBVZIkSZLUawZXSZIkSVKvGVwlSZIkSb1mcJUkSZIk9ZrBVZIkSZLUawZXSZIkSVKvGVwlSZIkSb1mcJUkSZIk9ZrBVZIkSZLUawZXSZIkSVKvGVwlSZIkSb1mcJUkSZIk9ZrBVZIkSZLUawZXSZIkSVKvGVwlSZIkSb1mcJUkSZIk9ZrBVZIkSZLUawZXSZIkSVKvTRtck6xO8skkNye5KclrWvkhSbYl2dHuV7TyJDkvyc4kNyQ5dmBfG1r9HUk2zF+3JEmSJEnjYibfuN4PvL6qng0cD5yd5GhgE3BlVa0FrmzrAKcAa9ttI3A+dEEXOAd4PnAccM5E2JUkSZIkaTLTBtequqOqPtuWvwncDKwC1gNbWrUtwGlteT1wUXWuApYnOQI4CdhWVfuq6m5gG3DynPZGkiRJkjR2ZvUb1yRrgOcBVwOHV9Ud0IVb4LBWbRVw+8DDdrWyycolSZIkSZrUjINrkicAfwG8tqq+MVXVIWU1Rfn+z7MxyfYk2/fu3TvT5kmSJEmSxtSMgmuSR9GF1vdV1Qdb8Z3tEmDa/Z5WvgtYPfDwI4HdU5Q/RFVdUFXrqmrdypUrZ9MXSZIkSdIYmsmswgEuBG6uqncMbNoKTMwMvAG4fKD8jDa78PHAve1S4o8BJyZZ0SZlOrGVSZIkSZI0qWUzqPMC4FXA55Nc18p+GzgXuDTJWcBtwMvbtiuAU4GdwH3AmQBVtS/JW4FrWr23VNW+OemFJEmSJGlsTRtcq+rvGf77VIAThtQv4OxJ9rUZ2DybBkqSJEmSlrZZzSosSZIkSdJCM7hKkiRJknrN4CpJkiRJ6jWDqyRJkiSp1wyukiRJkqReM7hKkiRJknrN4CpJkiRJ6jWDqyRJkiSp1wyukiRJkqReM7hKkiRJknrN4CpJkiRJ6jWDqyRJkiSp1wyukiRJkqReM7hKkiRJknrN4CpJkiRJ6jWDqyRJkiSp1wyukiRJkqReM7hKkiRJknrN4CpJkiRJ6jWDqyRJkiSp1wyukiRJkqReM7hKkiRJknrN4CpJkiRJ6jWDqyRJkiSp1wyukiRJkqReWzbqBkiSJGlyazZ9ZNRNkKSR8xtXSZIkSVKvGVwlSRojSW5N8vkk1yXZ3soOSbItyY52v6KVJ8l5SXYmuSHJsaNtvSRJwxlcJUkaPz9dVcdU1bq2vgm4sqrWAle2dYBTgLXtthE4f8FbKknSDBhcJUkaf+uBLW15C3DaQPlF1bkKWJ7kiFE0UJKkqRhcJUkaLwV8PMm1STa2ssOr6g6Adn9YK18F3D7w2F2t7CGSbEyyPcn2vXv3zmPTJUkazlmFJUkaLy+oqt1JDgO2JfniFHUzpKwOKKi6ALgAYN26dQdslyRpvvmNqyRJY6Sqdrf7PcCHgOOAOycuAW73e1r1XcDqgYcfCexeuNZKkjQzBldJksZEkscneeLEMnAicCOwFdjQqm0ALm/LW4Ez2uzCxwP3TlxSLElSn3ipsCRJ4+Nw4ENJoBvj319VH01yDXBpkrOA24CXt/pXAKcCO4H7gDMXvsmSJE3P4CpJ0pioqluA5w4p/zpwwpDyAs5egKZJkvSIeKmwJEmSJKnXDK6SJEmSpF4zuEqSJEmSes3gKkmSJEnqNYOrJEmSJKnXDK6SJEmSpF7zz+FIkiRJS8CaTR8ZdRMOcOu5Lxl1E7RI+I2rJEmSJKnXpg2uSTYn2ZPkxoGyQ5JsS7Kj3a9o5UlyXpKdSW5IcuzAYza0+juSbJif7kiSJEmSxs1MvnF9D3DyfmWbgCurai1wZVsHOAVY224bgfOhC7rAOcDzgeOAcybCriRJkiRJU5k2uFbVp4F9+xWvB7a05S3AaQPlF1XnKmB5kiOAk4BtVbWvqu4GtnFgGJYkSZIk6QAP9zeuh1fVHQDt/rBWvgq4faDerlY2WbkkSZIkSVOa68mZMqSspig/cAfJxiTbk2zfu3fvnDZOkiRJkrT4PNzgeme7BJh2v6eV7wJWD9Q7Etg9RfkBquqCqlpXVetWrlz5MJsnSZIkSRoXDze4bgUmZgbeAFw+UH5Gm134eODedinxx4ATk6xokzKd2MokSZIkSZrSsukqJLkYeBFwaJJddLMDnwtcmuQs4Dbg5a36FcCpwE7gPuBMgKral+StwDWt3luqav8JnyRJkiRJOsC0wbWqXjHJphOG1C3g7En2sxnUzXJLAAALXklEQVTYPKvWSZIkSZKWvLmenEmSJEmSpDllcJUkSZIk9ZrBVZIkSZLUawZXSZIkSVKvGVwlSZIkSb1mcJUkSZIk9ZrBVZIkSZLUawZXSZIkSVKvGVwlSZIkSb1mcJUkSZIk9ZrBVZIkSZLUawZXSZIkSVKvGVwlSZIkSb1mcJUkSZIk9ZrBVZIkSZLUawZXSZIkSVKvGVwlSZIkSb1mcJUkSZIk9ZrBVZIkSZLUawZXSZIkSVKvGVwlSZIkSb1mcJUkSZIk9ZrBVZIkSZLUawZXSZIkSVKvGVwlSZIkSb1mcJUkSZIk9ZrBVZIkSZLUawZXSZIkSVKvGVwlSZIkSb1mcJUkSZIk9ZrBVZIkSZLUa8tG3QBJkiRJS9OaTR8ZdRMOcOu5Lxl1EzSEwXWE/I8qSZIkSdMzuEqSJDV9PKksSfI3rpIkSZKknjO4SpIkSZJ6zeAqSZIkSeo1g6skSZIkqdcMrpIkSZKkXjO4SpIkSZJ6zeAqSZIkSeo1g6skSZIkqdcMrpIkSZKkXls26gZIkiRJUl+s2fSRUTfhALee+5JRN2HkFvwb1yQnJ/lSkp1JNi3080uSpIdybJYk9d2CfuOa5CDgj4H/E9gFXJNka1V9YSHbocn17QyTZ5ckaX6Ncmzu25gjSeqvhf7G9ThgZ1XdUlXfBS4B1i9wGyRJ0oMcmyVJvbfQv3FdBdw+sL4LeP4Ct0GLSB/PxvstsKQx49gsST3nZ+KFD64ZUlYPqZBsBDa21W8l+dIcPO+hwF1zsJ8+Gbc+LZr+5O0zqrZo+jML49ancesPjF+f5qM/T5/j/Y2DRzo2j9u/u8kslX7C0unrUukn2NdxNPJ+zvAz8UzMaGxe6OC6C1g9sH4ksHuwQlVdAFwwl0+aZHtVrZvLfY7auPXJ/vTfuPVp3PoD49encetPjz2isXmpHKel0k9YOn1dKv0E+zqOlko/By30b1yvAdYmOSrJwcDpwNYFboMkSXqQY7MkqfcW9BvXqro/yauBjwEHAZur6qaFbIMkSXqQY7MkaTFY6EuFqaorgCsW+Gnn9NLjnhi3Ptmf/hu3Po1bf2D8+jRu/emtRzg2L5XjtFT6CUunr0uln2Bfx9FS6ecDUlXT15IkSZIkaUQW+jeukiRJkiTNytgH1yQnJ/lSkp1JNo26PZNJsjrJJ5PcnOSmJK9p5Yck2ZZkR7tf0cqT5LzWrxuSHDuwrw2t/o4kG0bVp9aWg5J8LsmH2/pRSa5ubftAmwiEJI9u6zvb9jUD+3hjK/9SkpNG05MH2rI8yWVJvtiO1U8u5mOU5Dfav7cbk1yc5DGL7Rgl2ZxkT5IbB8rm7Jgk+T+SfL495rwkw/50yHz357+0f3M3JPlQkuUD24a+9pO99012fBe6TwPbfjNJJTm0rff+GAnae8Vnklzf3kN+b9Rtmm/ZbzwbR0lubf+XrkuyfdTtmU/DxvNRt2k+JHlWO54Tt28kee2o2zUfhn2mGXWb5kuS17R+3jSux3OoqhrbG90kE/8IPAM4GLgeOHrU7ZqkrUcAx7blJwL/ABwN/GdgUyvfBLy9LZ8K/DXd3987Hri6lR8C3NLuV7TlFSPs1+uA9wMfbuuXAqe35T8B/n1b/g/An7Tl04EPtOWj23F7NHBUO54HjbA/W4BfbcsHA8sX6zECVgFfBh47cGx+ebEdI+CFwLHAjQNlc3ZMgM8AP9ke89fAKSPoz4nAsrb89oH+DH3tmeK9b7Lju9B9auWr6SYE+gpw6GI5Rt6K9lo/oS0/CrgaOH7U7ZrnPj9kPBvHG3DrxP/Fcb8xZDwfdZsWoM8HAV8Dnj7qtsxD34Z+phl1u+aprz8O3Ag8jm6+or8B1o66XQtxG/dvXI8DdlbVLVX1XeASYP2I2zRUVd1RVZ9ty98Ebqb7T7ie7s2Vdn9aW14PXFSdq4DlSY4ATgK2VdW+qrob2AacvIBdeUCSI4GXAO9u6wFeDFzWquzfn4l+Xgac0OqvBy6pqu9U1ZeBnXTHdcEleRLdB/ALAarqu1V1D4v4GNG94T02yTK6N8A7WGTHqKo+Dezbr3hOjknb9qSq+l/VjRYXDexrwfpTVR+vqvvb6lV0f2dzoj/DXvuh733T/B+cN5McI4B3Am8ABidb6P0xErTj8622+qh2G9tJM/Yfz7S4TTGej7sTgH+sqq+MuiHzZP/PNLunqb9YPRu4qqrua58N/hb4uRG3aUGMe3BdBdw+sL6rlfVaukswn0d3BvvwqroDunALHNaqTda3PvX5v9J9KP1BW38KcM/AB/DBtj3Q7rb93la/T/15BrAX+LN2udi7kzyeRXqMquqrwB8At9EF1nuBa1ncx2jCXB2TVW15//JR+hW6bxVh9v2Z6v/ggkryMuCrVXX9fpvG4RgtCe3S2euAPXQnFa4edZvm0f7j2bgq4ONJrk2ycdSNmUeTjefj7nTg4lE3Yj4M+0xTVR8fbavmzY3AC5M8Jcnj6K5UWj3iNi2IcQ+uw37n1OszwkmeAPwF8Nqq+sZUVYeU1RTlCyrJS4E9VXXtYPGQqjXNtl70p1lGd7nj+VX1PODbdJehTqbXfUr3u8/1dJeYPhV4PHDKkKqL6RhNZ7Z96FXfkrwJuB9430TRkGq9708baN8E/O6wzUPKet+npaiqvl9Vx9BdAXBckh8fdZvmwyTj2bh6QVUdSzcWnJ3khaNu0DyZ7Xi+6LX5DF4G/I9Rt2U+DPtMk+SXRtuq+VFVN9P9bGgb8FG6nwPdP+WDxsS4B9ddPPQMxJH0+LKBJI+iC63vq6oPtuI726VwtPs9rXyyvvWlzy8AXpbkVrrLFF9Md8Z6ebuEY/+2PdDutv3JdJcW9qU/tLbsGvhW4TK6gW+xHqOfAb5cVXur6nvAB4F/yeI+RhPm6pjs4sHLcgfLF1ybjOilwCvbJbEw+/7cxeTHdyH9CN2Hi+vbe8SRwGeT/DCL+BgtVe0Sy08xup88zLcDxrMkfz7aJs2Pqtrd7vcAH2JEP81ZAJON5+PsFOCzVXXnqBsyTyb7TDOWqurCqjq2ql5I91lsx6jbtBDGPbheA6xts2geTHeJxNYRt2mo9tuzC4Gbq+odA5u2AhOzZ24ALh8oP6PNwHk83SURd9BNdHJikhXt7NOJrWxBVdUbq+rIqlpD97p/oqpeCXwS+PlWbf/+TPTz51v9auWnp5vR9ihgLd1ELAuuqr4G3J7kWa3oBOALLNJjRHc5zfFJHtf+/U30Z9EeowFzckzatm8mOb69RmcM7GvBJDkZ+C3gZVV138CmyV77oe997XhNdnwXTFV9vqoOq6o17T1iF93kdF9jkR6jpSbJyrTZrZM8lu5D4xdH26r5Mcl4Nnbf5CR5fJInTizT/R87YCbwcTDFeD7OXsGYXibcDPtMc/OI2zRvkhzW7p8G/BvG+9g+qHowQ9R83uiu+/4Huhk23zTq9kzRzn9Fd3nbDcB17XYq3W/SrqQ7k3IlcEirH+CPW78+D6wb2Nev0E3SshM4swd9exEPzir8DLoP1jvpLld5dCt/TFvf2bY/Y+Dxb2r9/BIjni0UOAbY3o7TX9LNbrpojxHwe3QfNm8E3ks3O+2iOkZ0b9Z3AN+jC0BnzeUxAda11+cfgT8CMoL+7KT7fefEe8OfTPfaM8l732THd6H7tN/2W3lwVuHeHyNvBfATwOfae+GNwO+Ouk0L1O8XMaazCrf3huvb7SZ6/Jlpjvp7wHg+6jbNY18fB3wdePKo2zLP/TzgM82o2zSPff07upMt1wMnjLo9C3VL67wkSZIkSb007pcKS5IkSZIWOYOrJEmSJKnXDK6SJEmSpF4zuEqSJEmSes3gKkmSJEnqNYOrJEmSJKnXDK6SJEmSpF4zuEqSJEmSeu1/A8HqaGS2Xsy/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize = (16, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(train_df['Price']);\n",
    "plt.title('Distribution of Price');\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(np.log1p(train_df['Price']));\n",
    "plt.title('Distribution of log of revenue');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cQIYyTmmV0K5"
   },
   "outputs": [],
   "source": [
    "train_df['id'] = range(0, 0+len(train_df))\n",
    "test_df['id'] = range(0, 0+len(test_df))\n",
    "\n",
    "## Count features\n",
    "for col in ['Author', 'BookCategory', 'Edition', 'Genre',  'Ratings',\n",
    "          'Reviews',         \n",
    "            ['Author', 'BookCategory'], \n",
    "            ['Author', 'Edition'], \n",
    "            ['Author', 'Genre'],\n",
    "            ['Author', 'Ratings'],\n",
    "            ['Author', 'Reviews'],\n",
    "           \n",
    "\n",
    "            \n",
    "            \n",
    "\n",
    "           ]:\n",
    "    if not isinstance(col, list):\n",
    "        col = [col]\n",
    "    col_name = \"_\".join(col)\n",
    "    all_df = pd.concat([train_df[[\"id\"]+ col], test_df[[\"id\"]+ col]])\n",
    "    gdf = all_df.groupby(col)[\"id\"].count().reset_index()\n",
    "    gdf.columns = col + [col_name+\"_count\"]\n",
    "    train_df = pd.merge(train_df, gdf, on=col, how=\"left\")\n",
    "    test_df = pd.merge(test_df, gdf, on=col, how=\"left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dsg9G3dXTDqx"
   },
   "outputs": [],
   "source": [
    "train_df['log_Price'] = np.log1p(train_df['Price'])\n",
    "test_df['log_Price'] = np.log1p(test_df['Price'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "2z75HA24TwgK",
    "outputId": "2206801f-1d08-49de-f58c-e056914faa7d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6237, 1128) (1560, 1128)\n"
     ]
    }
   ],
   "source": [
    "print(train_df.shape, test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Cyuc7TQywobJ"
   },
   "outputs": [],
   "source": [
    "class renamer():\n",
    "  def __init__(self):\n",
    "    self.d = dict()\n",
    "\n",
    "  def __call__(self, x):\n",
    "    if x not in self.d:\n",
    "      self.d[x] = 0\n",
    "      return x\n",
    "    else:\n",
    "      self.d[x] += 1\n",
    "      return \"%s_%d\" % (x, self.d[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "inXoKjE64_Es"
   },
   "outputs": [],
   "source": [
    "train_df.rename(columns=renamer(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "aFz5I29s5X0k"
   },
   "outputs": [],
   "source": [
    "test_df.rename(columns=renamer(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 33
    },
    "colab_type": "code",
    "id": "veboFaPdkJIA",
    "outputId": "eba2d394-12a0-414b-c424-13626024b7e2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6237, 1128) (1560, 1128)\n"
     ]
    }
   ],
   "source": [
    "print(train_df.shape, test_df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lHTqdqhK2V0T"
   },
   "outputs": [],
   "source": [
    "def mean_likelihood(df, cat_var, target, alpha = 0.5):\n",
    "    P_c = df.groupby(cat_var)[target].transform('mean')\n",
    "    P_global = df[target].mean()\n",
    "    n_c = df.groupby(cat_var)[target].transform('count')\n",
    "    enc = (P_c*n_c + P_global*alpha)/(n_c + alpha)\n",
    "    temp = df[[cat_var]]\n",
    "    temp['enc'] = enc\n",
    "    return temp.groupby(cat_var).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "siBY1FSM2Z7Y"
   },
   "outputs": [],
   "source": [
    "cat_vars = ['Author', 'Genre', 'Ratings', 'Reviews', 'BookCategory', 'Edition_Type', 'Year', 'Month']\n",
    "# getting mean encoding features\n",
    "cvlist = list(KFold(n_splits = 10, random_state = 1).split(train_df))\n",
    "for var in cat_vars:\n",
    "    mean_enc_var = np.zeros(len(train_df))\n",
    "    for tr_idx, val_idx in cvlist:\n",
    "        X_tr, X_val = train_df.loc[tr_idx], train_df.loc[val_idx]\n",
    "        X_tr_mean = mean_likelihood(X_tr, var, 'log_Price')\n",
    "        mean_enc_var[val_idx] = X_val[var].map(X_tr_mean['enc'])\n",
    "        train_df[f'mean_enc_{var}'] = mean_enc_var\n",
    "    train_df[f'mean_enc_{var}'] = train_df[f'mean_enc_{var}'].fillna(train_df[f'mean_enc_{var}'].mean())\n",
    "    test_df[f'mean_enc_{var}'] = test_df[var].map(mean_likelihood(train_df, \n",
    "                                                                    var, 'log_Price')['enc'])\n",
    "    test_df[f'mean_enc_{var}'] = test_df[f'mean_enc_{var}'].fillna(train_df[f'mean_enc_{var}'].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jEUzHogCllKT"
   },
   "source": [
    "###  Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bewxgTDDlgbK"
   },
   "outputs": [],
   "source": [
    "train_X = train_df.drop(['is_train','Price', 'log_Price','Synopsis','Title', 'Date', 'Edition', 'BookCategory', 'Edition_Type',  'Month','id'],axis=1)\n",
    "test_X = test_df.drop(['is_train', 'Price', 'log_Price', 'Synopsis', 'Title','Date', 'Edition','BookCategory', 'Edition_Type',  'Month', 'id'],axis=1)\n",
    "y = (train_df['log_Price'])\n",
    "train_y = y\n",
    "\n",
    "\n",
    "X = train_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1eL5_GRcoqBk"
   },
   "outputs": [],
   "source": [
    "def runLGB(train_X, train_y, test_X, test_y=None, test_X2=None): \n",
    "    params = {}\n",
    "    params[\"objective\"] = \"regression\"\n",
    "    params['metric'] = 'rmse'\n",
    "    params[\"max_depth\"] = 8\n",
    "    params[\"min_data_in_leaf\"] = 1\n",
    "    params[\"learning_rate\"] = 0.0025\n",
    "    params[\"bagging_fraction\"] = 0.8\n",
    "    params[\"feature_fraction\"] = 0.8\n",
    "    params[\"bagging_freq\"] = 5\n",
    "    params[\"bagging_seed\"] = 0\n",
    "    params[\"verbosity\"] = -1\n",
    "    num_rounds = 20000\n",
    "\n",
    "    plst = list(params.items())\n",
    "    lgtrain = lgb.Dataset(train_X, label=train_y)\n",
    "\n",
    "    if test_y is not None:\n",
    "        lgtest = lgb.Dataset(test_X, label=test_y)\n",
    "        model = lgb.train(params, lgtrain,  num_rounds, valid_sets=[lgtest], \n",
    "                          early_stopping_rounds=200, verbose_eval=1000)\n",
    "    else:\n",
    "        lgtest = lgb.Dataset(test_X)\n",
    "        model = lgb.train(params, lgtrain,   num_rounds)\n",
    "\n",
    "    pred_test_y = model.predict(test_X, num_iteration=model.best_iteration)\n",
    "    pred_test_y2 = model.predict(test_X2, num_iteration=model.best_iteration)\n",
    "\n",
    "    loss = 0\n",
    "    if test_y is not None:\n",
    "        loss = np.sqrt(metrics.mean_squared_error(test_y, pred_test_y))\n",
    "        print(loss)\n",
    "        return pred_test_y, loss, pred_test_y2, model\n",
    "    else:\n",
    "        return pred_test_y, loss, pred_test_y2, model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "c_OuI8-xou8Y",
    "outputId": "9a160d4a-cf5f-45d5-dfc9-c4ce2dc26678"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 200 rounds.\n",
      "[1000]\tvalid_0's rmse: 0.520673\n",
      "[2000]\tvalid_0's rmse: 0.496192\n",
      "[3000]\tvalid_0's rmse: 0.488982\n",
      "[4000]\tvalid_0's rmse: 0.485267\n",
      "[5000]\tvalid_0's rmse: 0.483064\n",
      "[6000]\tvalid_0's rmse: 0.481801\n",
      "[7000]\tvalid_0's rmse: 0.480791\n",
      "[8000]\tvalid_0's rmse: 0.480082\n",
      "[9000]\tvalid_0's rmse: 0.47954\n",
      "[10000]\tvalid_0's rmse: 0.47909\n",
      "[11000]\tvalid_0's rmse: 0.478765\n",
      "[12000]\tvalid_0's rmse: 0.478467\n",
      "[13000]\tvalid_0's rmse: 0.478206\n",
      "Early stopping, best iteration is:\n",
      "[13312]\tvalid_0's rmse: 0.478125\n",
      "0.47812507361392914\n",
      "[0.47812507361392914]\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[1000]\tvalid_0's rmse: 0.499831\n",
      "[2000]\tvalid_0's rmse: 0.477791\n",
      "[3000]\tvalid_0's rmse: 0.471628\n",
      "[4000]\tvalid_0's rmse: 0.468586\n",
      "[5000]\tvalid_0's rmse: 0.466549\n",
      "[6000]\tvalid_0's rmse: 0.465412\n",
      "[7000]\tvalid_0's rmse: 0.464683\n",
      "[8000]\tvalid_0's rmse: 0.464205\n",
      "[9000]\tvalid_0's rmse: 0.463778\n",
      "Early stopping, best iteration is:\n",
      "[9488]\tvalid_0's rmse: 0.463663\n",
      "0.4636631108016663\n",
      "[0.47812507361392914, 0.4636631108016663]\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[1000]\tvalid_0's rmse: 0.514413\n",
      "[2000]\tvalid_0's rmse: 0.493209\n",
      "[3000]\tvalid_0's rmse: 0.487319\n",
      "[4000]\tvalid_0's rmse: 0.484978\n",
      "[5000]\tvalid_0's rmse: 0.483071\n",
      "[6000]\tvalid_0's rmse: 0.48193\n",
      "[7000]\tvalid_0's rmse: 0.481141\n",
      "[8000]\tvalid_0's rmse: 0.480486\n",
      "[9000]\tvalid_0's rmse: 0.480012\n",
      "Early stopping, best iteration is:\n",
      "[9523]\tvalid_0's rmse: 0.479842\n",
      "0.4798423217495276\n",
      "[0.47812507361392914, 0.4636631108016663, 0.4798423217495276]\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[1000]\tvalid_0's rmse: 0.511413\n",
      "[2000]\tvalid_0's rmse: 0.485596\n",
      "[3000]\tvalid_0's rmse: 0.47874\n",
      "[4000]\tvalid_0's rmse: 0.475551\n",
      "[5000]\tvalid_0's rmse: 0.473558\n",
      "[6000]\tvalid_0's rmse: 0.472358\n",
      "[7000]\tvalid_0's rmse: 0.471596\n",
      "[8000]\tvalid_0's rmse: 0.47107\n",
      "[9000]\tvalid_0's rmse: 0.470519\n",
      "[10000]\tvalid_0's rmse: 0.47026\n",
      "Early stopping, best iteration is:\n",
      "[10435]\tvalid_0's rmse: 0.470121\n",
      "0.4701207693687143\n",
      "[0.47812507361392914, 0.4636631108016663, 0.4798423217495276, 0.4701207693687143]\n",
      "Training until validation scores don't improve for 200 rounds.\n",
      "[1000]\tvalid_0's rmse: 0.555016\n",
      "[2000]\tvalid_0's rmse: 0.532276\n",
      "[3000]\tvalid_0's rmse: 0.524858\n",
      "[4000]\tvalid_0's rmse: 0.521095\n",
      "[5000]\tvalid_0's rmse: 0.51899\n",
      "[6000]\tvalid_0's rmse: 0.517281\n",
      "[7000]\tvalid_0's rmse: 0.516175\n",
      "[8000]\tvalid_0's rmse: 0.515339\n",
      "[9000]\tvalid_0's rmse: 0.514701\n",
      "[10000]\tvalid_0's rmse: 0.514309\n",
      "[11000]\tvalid_0's rmse: 0.5139\n",
      "[12000]\tvalid_0's rmse: 0.513724\n",
      "Early stopping, best iteration is:\n",
      "[11844]\tvalid_0's rmse: 0.513701\n",
      "0.5137005392401351\n",
      "[0.47812507361392914, 0.4636631108016663, 0.4798423217495276, 0.4701207693687143, 0.5137005392401351]\n",
      "0.4810903629547945\n"
     ]
    }
   ],
   "source": [
    "cv_scores = []\n",
    "pred_test_full = 0\n",
    "kf = model_selection.KFold(n_splits=5, shuffle=True, random_state=30)\n",
    "for dev_index, val_index in kf.split(X, y):  \n",
    "    dev_X, val_X = train_X.loc[dev_index,:], train_X.loc[val_index,:]\n",
    "    dev_y, val_y = train_y[dev_index], train_y[val_index]\n",
    "    \n",
    "    pred_val, loss, pred_test,model = runLGB(dev_X, dev_y, val_X, val_y, test_X)\n",
    "    pred_test_full += pred_test\n",
    "    cv_scores.append(loss)\n",
    "    print(cv_scores)\n",
    "pred_test_full /= 5.\n",
    "print(sum(cv_scores)/5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "445DI22Yow-w"
   },
   "outputs": [],
   "source": [
    "# # Fill the is_pass variable with the predictions\n",
    "sub_df['Price']= pd.DataFrame(np.exp(pred_test_full))\n",
    "\n",
    "# # Converting the submission file to excel format\n",
    "sub_df.to_excel('lgb_101_bs_0_lr_0p0025_final_2.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "MH_Price_v0_797830.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "pygments_lexer": "ipython3",
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